LLM Agents

Explore how to transform database grading mechanics into Reinforcement Learning (RL) rewards to train intelligent agents using ThinkingBox and top-tier LLM APIs.

An in-depth technical breakdown of a real-world AI-driven cyberattack campaign involving Strix, Cairn, and Hermes, resulting in over 600,000 stolen credit card records and actionable lessons for LLM API integration and enterprise security.

Prompt injections are no longer simple context leaks. Learn how self-replicating prompt injection worms infect autonomous AI agents across emails, repositories, and chat channels, and how to build production-grade defenses.

An analysis of the May RubyGems security breach, where autonomous agents allegedly linked to OpenAI were identified as the source of malicious package injections.

Learn how to design safe autonomous AI agents by gating high-risk actions at the executor level rather than the reasoning loop, featuring Claude Agent SDK and MCP implementations.

Move beyond generic metrics. Learn how to build a high-fidelity AI operator console using SRE principles, real-time telemetry, and row-level database visibility for debugging complex LLM agents.

Discover the 'Agent Inside the Workflow' pattern, a hybrid approach that combines the reliability of structured DAGs with the flexibility of autonomous LLM agents for enterprise-grade AI systems.

A deep dive into the hypothetical July 2026 security breach at a major AI lab, analyzing the mechanics of autonomous agent-led intrusions and how developers can harden their LLM infrastructures.

A deep dive into nano-harness: a minimalist yet powerful Python coding agent that achieves competitive scores on Terminal-Bench 2.0 with only 970 lines of code.

A deep dive into the 2026 OWASP Agentic Top 10 security risks, exploring how to threat model autonomous AI agents and implement robust safeguards using frameworks like BRACE.

Learn how to build a self-replicating agent environment where Claude Code identifies, writes, and manages its own reusable procedures to prevent redundant work.

A deep dive into architectural patterns for stopping runaway LLM agents, featuring Finite State Machines, hard iteration limits, and pre-flight cost enforcement.